import plotly.express as px
import numpy as np
import plotly.graph_objects as go
import networkx as nx
import itertools
import pathpy as pp
import ast
def temporal_walk(tempnet, l=10, start_node=None, end_nodes=[]):
itinerary = []
if start_node is None:
current_node = np.random.choice(tempnet.nodes)
else:
current_node = start_node
itinerary.append(current_node)
steps = 0
for t in tempnet.ordered_times:
prev_node = current_node
# find possible targets in time t
targets = set()
targets.add(False)
for (v, w, time) in tempnet.time[t]:
if v == current_node:
targets.add(w)
# move to random target or stay if False is extracted
if targets:
rand_node = np.random.choice(list(targets))
if (rand_node!=False):
current_node = rand_node
steps += 1
# add currently visited node
if current_node != prev_node:
itinerary.append(current_node)
if steps == l:
break
if current_node in end_nodes :
break
return itinerary
from pathpy.classes.paths import Paths
def paths_from_temporal_walk(network, l, n=100, start_node=None, end_nodes=[]):
"""
Generates n paths of a random walker in the given network
and returns them as a paths object.
Each path has a length of l steps.
Parameters
----------
network: Network, TemporalNetwork, HigherOrderNetwork
The network structure on which the random walks will be simulated.
int: l
The (maximum) length of each random walk path. A path will
terminate if a node with outdegree zero is reached.
int: n
The number of random walk paths to generate.
"""
p = pp.Paths()
for i in range(n):
path = temporal_walk(network, l, start_node, end_nodes)
p.add_path(tuple(path))
return p
file=open(r"C:\Users\Daniele\Desktop\Graph mining\icalp.tg")
max_v=0
for line in file:
a=line.split()
max_v=max(max_v,int(a[0]),int(a[1]))
G=nx.Graph()
G.add_nodes_from(range(1,max_v+1))
file=open(r"C:\Users\Daniele\Desktop\Graph mining\icalp.tg")
for line in file:
a=line.split()
G.add_edge(int(a[0]),int(a[1]))
nx.Graph.number_of_nodes(G)
4669
largest_cc = max(nx.connected_components(G), key=len)
G=G.subgraph(largest_cc)
nx.Graph.number_of_nodes(G)
3458
# TAKE ABOUT 5 MINUTES TO BE COMPUTED
#dict_centrality=nx.algorithms.centrality.betweenness_centrality(G,seed=42)
# f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictCentrality.txt", "w")
# f.write( str(dict_centrality) )
# f.close()
f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictCentrality.txt", "r")
contents = f.read()
dict_centrality = ast.literal_eval(contents)
f.close()
with open(r"C:\Users\Daniele\Desktop\Graph mining\graphs\icalpw\icalpw2021map.txt",'r') as f:
lines = f.readlines()
id_cc=[]
for line in lines:
id_cc.append(int(line))
with open(r"C:\Users\Daniele\Desktop\Graph mining\icalp_id_author.txt",'r') as f:
lines = f.readlines()
dict_names={}
for line in lines:
dict_names[int(line.split(" ")[0])]=line.strip(" 1234567890").replace("\n","")
{k: v for k, v in sorted(dict_centrality.items(), key=lambda item: item[1],reverse=True)}
{2662: 0.09954252305149089,
487: 0.08918272157500713,
381: 0.060897925937106356,
1198: 0.06001497845149456,
1856: 0.05960307214517947,
944: 0.05776552614088715,
523: 0.050441745517729945,
1857: 0.048732833724900386,
1491: 0.04657762193797237,
2189: 0.04432095133942769,
78: 0.04369316061814471,
368: 0.042424282232685726,
1355: 0.04128099868417877,
1625: 0.03881459653752921,
1100: 0.03485880838916979,
1713: 0.0341935220401168,
1240: 0.033930643982892965,
1915: 0.03336169987029349,
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{dict_names[k]: v for k, v in sorted(dict_centrality.items(), key=lambda item: item[1],reverse=True)}
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'Daniel Lehmann 0001': 0.0011569889060307054,
'Louis E. Rosier': 0.0011569889060307054,
'Azaria Paz': 0.001156905205755365,
'Christian Choffrut': 0.001156905205755365,
'Max Dauchet': 0.001156905205755365,
'Silvio Micali': 0.001156905205755365,
'Bernhard Steffen': 0.001156905205755365,
'Jarkko Kari': 0.001156905205755365,
'Bin Fu': 0.001156905205755365,
'Andrei Voronkov': 0.001156905205755365,
'Björn Victor': 0.001156905205755365,
'Sanjeev Arora': 0.001156905205755365,
'Douglas Wikström': 0.001156905205755365,
'Oded Regev 0001': 0.001156905205755365,
'Dirk Nowotka': 0.001156905205755365,
'Stanislav Zivný': 0.001156905205755365,
'Igor Shinkar': 0.001156905205755365,
'Prasad Raghavendra': 0.001156905205755365,
'Keren Censor-Hillel': 0.001156905205755365,
'Dirk Pattinson': 0.0011569052057553647,
'Wan J. Fokkink': 0.0011567936053882442,
'Seymour Ginsburg': 0.001156737805204684,
'Werner Damm': 0.001156737805204684,
'Yacov Yacobi': 0.001156737805204684,
'Heikki Mannila': 0.001156737805204684,
'Paris C. Kanellakis': 0.001156737805204684,
'Wolfgang Maass 0001': 0.001156737805204684,
'Frits W. Vaandrager': 0.001156737805204684,
'Pavel Pudlák': 0.001156737805204684,
'Derek G. Corneil': 0.001156737805204684,
'Walid Taha': 0.001156737805204684,
'Yoram Hirshfeld': 0.001156737805204684,
'Lars Engebretsen': 0.001156737805204684,
'Anil Vullikanti': 0.001156737805204684,
'Juha Kärkkäinen': 0.001156737805204684,
'René Sitters': 0.001156737805204684,
'Kristoffer Arnsfelt Hansen': 0.001156737805204684,
'Piotr Sankowski': 0.001156737805204684,
'David Doty': 0.001156737805204684,
'Carmine Ventre': 0.001156737805204684,
'Julia Kempe': 0.001156737805204684,
'Rajat Mittal 0001': 0.001156737805204684,
'Ross Duncan': 0.001156737805204684,
'Jop Briët': 0.001156737805204684,
'Valia Mitsou': 0.001156737805204684,
'Amin Karbasi': 0.001156737805204684,
'Justin Thaler': 0.001156737805204684,
'Suzanne van der Ster': 0.001156737805204684,
'Aditya Bhaskara': 0.001156737805204684,
'Jayalal Sarma': 0.001156737805204684,
'Arnaud Sangnier': 0.001156737805204684,
'Shahbaz Khan 0004': 0.001156737805204684,
'Adam Kurpisz': 0.001156737805204684,
'Yun Kuen Cheung': 0.001156737805204684,
'Cosimo Vinci': 0.001156737805204684,
'Reino Niskanen': 0.001156737805204684,
'Ran Ben-Basat': 0.001156737805204684,
'Helmut Prodinger': 0.0011567378052046839,
'Guy Louchard': 0.0011567378052046839,
'Nicolas Bonichon': 0.0011567378052046839,
'Stuart W. Margolis': 0.0011565704046540032,
'Christos Tzamos': 0.0011505303590915736,
'Meital Levy': 0.0011417359585056185,
'Paul Beame': 0.0011347873217723392,
'Loukas Georgiadis': 0.0011319204707662225,
'Dominik Pajak': 0.001128989780127861,
'Sang-il Oum': 0.0011209598691443922,
'Michal Wlodarczyk 0001': 0.001119185377571312,
'Chen Avin': 0.0011159953473427091,
'Ioannis Panageas': 0.0011157526316585846,
'Slawomir Lasota 0001': 0.0010986281952942902,
'Hsin-Hao Su': 0.0010944555410071202,
'Christoph Koch 0001': 0.001088014210945696,
'Michael Pinsker': 0.0010873241099229902,
'Zhenjian Lu': 0.001078180680430374,
'Marie-Pierre Béal': 0.0010758879169371898,
'Michael Schapira': 0.001066525541965224,
'Stéphanie Delaune': 0.0010655587753712226,
'Andrew V. Goldberg': 0.001055956898114929,
'Xi Chen 0001': 0.0010460867240293123,
'Jörg-Rüdiger Sack': 0.0010418884116981052,
'Rajiv Ramaswami': 0.0010405961005078004,
'Giora Slutzki': 0.0010363032991665133,
'Karol Wegrzycki': 0.0010202202510753713,
'Andrew Drucker': 0.0010178435852248138,
'Noa Lewenstein': 0.001014335069824552,
'Karianto Wong': 0.001010589481246721,
'Daniel Yankelevich': 0.0010002761037875825,
'Joseph Cheriyan': 0.0009914363636930833,
'Grigory Yaroslavtsev': 0.0009898240155168194,
'Danupon Nanongkai': 0.0009804868502976468,
'Omer Reingold': 0.0009704011018738832,
'Süleyman Cenk Sahinalp': 0.0009698962196261555,
'Thomas Kesselheim': 0.0009697004930146931,
'David A. Mix Barrington': 0.0009662707429694376,
'Emmanuel Filiot': 0.0009643818192155107,
'Mireille Clerbout': 0.0009633964736553385,
'Yves Roos': 0.0009633964736553385,
'Klaus W. Wagner': 0.000958441521446102,
'Gilles Barthe': 0.0009543096519057608,
'Bodo Manthey': 0.0009497021597421047,
'Jean Berstel': 0.0009292142971807483,
'Anna Adamaszek': 0.0009232935755238293,
'Venkatesh Raman 0001': 0.0009085305610656142,
'Martin Rötteler': 0.0009070006520935269,
'David Harel': 0.000902259555802652,
'Sandeep Sen': 0.0008989619132283157,
'Antoine Petit 0001': 0.0008945313393060452,
'Ilan Reuven Cohen': 0.0008915145595994367,
'Per Austrin': 0.0008903419616764746,
'Helmut Alt': 0.0008900738014886988,
'Véronique Bruyère': 0.0008870775179160513,
'Gianpaolo Oriolo': 0.0008811632709269177,
'Alina Ene': 0.000881160426260753,
'Peter Pepper': 0.0008799851783055757,
'Lijie Chen 0001': 0.0008782166181997589,
'Giovanni Di Crescenzo': 0.0008764235773819052,
'Markus Bläser': 0.0008745122388628942,
'Maurizio Patrignani': 0.0008732213056590539,
'Jan A. Bergstra': 0.0008679997553719953,
'Maria-Florina Balcan': 0.0008676370541788534,
'Sergio Greco': 0.0008676370541788533,
'Jeffery I. Zucker': 0.0008666047507829882,
'Meirav Zehavi': 0.0008364704360076324,
'Denis Kuperberg': 0.0008318688283652271,
'Jesper Buus Nielsen': 0.0008298499075694518,
'Martin Otto 0001': 0.0008194224544309659,
'Mohit Singh': 0.0008105422707474026,
'Amos Korman': 0.0008100663205846771,
'Sarel Cohen': 0.0008063747083517926,
'Roland Meyer 0001': 0.0008051602464462462,
'Sariel Har-Peled': 0.0007980657100390547,
'Yinzhan Xu': 0.0007953803817574685,
'Moran Feldman': 0.0007811187529969993,
'Eva Rotenberg': 0.000780104331457265,
'Karel Král 0002': 0.0007778876027085797,
'Anna C. Gilbert': 0.0007705851809577306,
'Gerth Stølting Brodal': 0.0007698317043289627,
'Afonso Ferreira': 0.0007694617015012811,
'Avner Magen': 0.0007671291737359548,
'Arturs Backurs': 0.0007661629189354604,
'Keerti Choudhary': 0.0007559645380412124,
'Jesper Nederlof': 0.0007509833499007657,
'Yair Bartal': 0.0007499771541326107,
'Sylvain Lombardy': 0.0007474066159629044,
'Yi Wu 0002': 0.0007437065348039572,
'Ravi Sethi': 0.000739779607119842,
'Adam Polak 0001': 0.0007346834334440608,
'Andreas Galanis': 0.0007309482911800709,
'Tongyang Li': 0.0007304547041202141,
'Edon Kelmendi': 0.0007292305178194355,
'Artur Jez': 0.000721593730748629,
'Tomasz Radzik': 0.0007212589239272319,
'Benoît Larose': 0.0007191855280618152,
'Ravi Sundaram': 0.0007172385322061166,
'Christoph Ambühl': 0.000713378809751496,
'F. Frances Yao': 0.0007132032464325193,
'Peng Zhang': 0.0007120100175365263,
'Erik Meineche Schmidt': 0.0007093268364352106,
'Farid M. Ablayev': 0.000708248715213793,
'Kostas Kollias': 0.0007006666046893931,
'Jochen Könemann': 0.0006903505387594023,
'Milind A. Sohoni': 0.000685393245150679,
'Jirí Fiala 0001': 0.0006835097154588569,
'Shyan Akmal': 0.0006824113582460765,
'S. Anand 0002': 0.0006809700268507226,
'Wing-Kin Sung': 0.0006742161342642895,
'Arnaud Labourel': 0.0006735366530732272,
'Giulio Manzonetto': 0.0006727043934204684,
'Ofer Neiman': 0.0006716137774197511,
'Ioannis Caragiannis': 0.0006701078267786553,
'Aarthi Sundaram': 0.0006576537421296297,
'Michael E. Saks': 0.0006564268009384174,
'James Riely': 0.0006494648938791296,
'Christoph Berkholz': 0.0006481242705809047,
'Wojciech Plandowski': 0.0006478528965136569,
'Jerzy Marcinkowski': 0.0006455109813501906,
'Alexander Okhotin': 0.0006423337756569947,
'S. Srinivasa Rao 0001': 0.0006413638072551757,
'Andreas Emil Feldmann': 0.0006390997654300149,
'Rolf G. Karlsson': 0.0006300865275981455,
'Ramakrishna Thurimella': 0.0006299716185158774,
'Kazuhisa Makino': 0.0006291185694239985,
'Mahsa Shirmohammadi': 0.0006186942242014747,
...}
authors_algorithms=[487,1198,1856,2189,78]
for auth in authors_algorithms:
print(dict_names[auth])
Paul G. Spirakis Piotr Indyk Fedor V. Fomin Dániel Marx Kurt Mehlhorn
authors_formal_methods=[381,944,1625,1240,1333]
for auth in authors_formal_methods:
print(dict_names[auth])
Donald Sannella Anca Muscholl James Worrell 0001 Christel Baier Igor Walukiewicz
authors_networks=[2662,1857,1100,1215,1152]
for auth in authors_networks:
print(dict_names[auth])
Ioannis Chatzigiannakis Mohammad Taghi Hajiaghayi Stefano Leonardi Christos Kaklamanis Michael Mitzenmacher
authors_cryptography=[523,1491,930,1588, 738]
for auth in authors_cryptography:
print(dict_names[auth])
Moti Yung Rafail Ostrovsky Giuseppe Persiano Yuval Ishai Gilles Brassard
authors_chosen=authors_algorithms+authors_formal_methods+authors_networks+authors_cryptography
map_auth_consolidate = {}
for i in range(1,max_v+1):
if (i in authors_algorithms or i in authors_formal_methods or i in authors_networks or i in authors_cryptography):
if (i in authors_algorithms):
map_auth_consolidate[i]=5000
elif (i in authors_formal_methods):
map_auth_consolidate[i]=6000
elif (i in authors_networks):
map_auth_consolidate[i]=7000
else:
map_auth_consolidate[i]=8000
else:
map_auth_consolidate[i]=i
file=open(r"C:\Users\Daniele\Desktop\Graph mining\icalp.tg")
tg=pp.TemporalNetwork()
for line in file:
l=line.split()
u=int(l[0])
v=int(l[1])
time=int(l[2])
if (u in largest_cc and v in largest_cc):
if ( map_auth_consolidate[u] == map_auth_consolidate[v] ):
continue
tg.add_edge(map_auth_consolidate[u],map_auth_consolidate[v],time)
tg.add_edge(map_auth_consolidate[v],map_auth_consolidate[u],time)
print(tg)
Nodes: 3442 Time-stamped links: 18574 Links/Nodes: 5.396281231841952 Observation period: [1972, 2021] Observation length: 49 Time stamps: 48 Avg. inter-event dt: 1.0425531914893618 Min/Max inter-event dt: 1/2
def get_frequencies(tg,l,n,start_node,end_nodes):
prw = paths_from_temporal_walk(tg,l,n,start_node,end_nodes)
count=0
f_algo=0
f_formal=0
f_net=0
f_crypto=0
for l in prw.paths:
for x in prw.paths[l]:
if prw.paths[l][x][1]>0 and int(x[-1]) in end_nodes:
count= count+ prw.paths[l][x][1]
if int(x[-1])==5000:
f_algo= f_algo + prw.paths[l][x][1]
elif int(x[-1])==6000:
f_formal= f_formal + prw.paths[l][x][1]
elif int(x[-1])==7000:
f_net= f_net + prw.paths[l][x][1]
else:
f_crypto= f_crypto + prw.paths[l][x][1]
if count>0:
return (f_algo/count,f_formal/count,f_net/count,f_crypto/count)
else:
return (0,0,0,0)
# TOOK ABOUT 10 HOURS TO BE COMPUTED
# dict_frequency={}
# for auth in authors_algorithms:
# dict_frequency[auth]=(1,0,0,0)
# for auth in authors_formal_methods:
# dict_frequency[auth]=(0,1,0,0)
# for auth in authors_networks:
# dict_frequency[auth]=(0,0,1,0)
# for auth in authors_cryptography:
# dict_frequency[auth]=(0,0,0,1)
# for vertex in [ v for v in largest_cc if v not in authors_chosen ]:
# dict_frequency[vertex]=get_frequencies(tg,l=7,n=3000,start_node=vertex,end_nodes=[5000,6000,7000,8000])
# f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies.txt", "w")
# f.write( str(dict_frequency) )
# f.close()
f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies.txt", "r")
contents = f.read()
dict_frequency = ast.literal_eval(contents)
f.close()
dict_frequency_2={}
for vertex in largest_cc:
(a,b,c,d)=dict_frequency[vertex]
dict_frequency_2[vertex]=(1 if a>=0.9 else 0, 1 if b>=0.9 else 0,1 if c>=0.9 else 0,1 if d>=0.9 else 0)
aut_classified=[[],[],[],[]]
aut_to_classify=[]
for key,value in dict_frequency_2.items():
if value==(0,0,0,0):
aut_to_classify.append(key)
else:
aut_classified[np.argmax(value)].append(key)
min_cat=min(len(aut_classified[0]),len(aut_classified[1]),len(aut_classified[2]),len(aut_classified[3]))
aut_algo_temp=sorted(aut_classified[0], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[0])-set(aut_algo_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[0]=aut_algo_temp
aut_formal_temp=sorted(aut_classified[1], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[1])-set(aut_formal_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[1]=aut_formal_temp
aut_net_temp=sorted(aut_classified[2], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[2])-set(aut_net_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[2]=aut_net_temp
aut_crypto_temp=sorted(aut_classified[3], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[3])-set(aut_crypto_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[3]=aut_crypto_temp
map_auth_consolidate = {}
for i in range(1,max_v+1):
if (i in aut_classified[0] or i in aut_classified[1] or i in aut_classified[2] or i in aut_classified[3]):
if (i in aut_classified[0]):
map_auth_consolidate[i]=5000
elif (i in aut_classified[1]):
map_auth_consolidate[i]=6000
elif (i in aut_classified[2]):
map_auth_consolidate[i]=7000
else:
map_auth_consolidate[i]=8000
else:
map_auth_consolidate[i]=i
file=open(r"C:\Users\Daniele\Desktop\Graph mining\icalp.tg")
tg=pp.TemporalNetwork()
for line in file:
l=line.split()
u=int(l[0])
v=int(l[1])
time=int(l[2])
if (u in largest_cc and v in largest_cc):
if ( map_auth_consolidate[u] == map_auth_consolidate[v] ):
continue
tg.add_edge(map_auth_consolidate[u],map_auth_consolidate[v],time)
tg.add_edge(map_auth_consolidate[v],map_auth_consolidate[u],time)
print(tg)
Nodes: 2838 Time-stamped links: 15650 Links/Nodes: 5.514446793516561 Observation period: [1972, 2021] Observation length: 49 Time stamps: 48 Avg. inter-event dt: 1.0425531914893618 Min/Max inter-event dt: 1/2
#started 15:30
# for vertex in aut_to_classify:
# dict_frequency_2[vertex]=get_frequencies(tg,l=7,n=3000,start_node=vertex,end_nodes=[5000,6000,7000,8000])
#end 18:
# f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies2.txt", "w")
# f.write( str(dict_frequency_2) )
# f.close()
f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies2.txt", "r")
contents = f.read()
dict_frequency_2 = ast.literal_eval(contents)
f.close()
dict_frequency_3={}
for vertex in largest_cc:
(a,b,c,d)=dict_frequency_2[vertex]
dict_frequency_3[vertex]=(1 if a>=0.9 else 0, 1 if b>=0.9 else 0,1 if c>=0.9 else 0,1 if d>=0.9 else 0)
aut_classified=[[],[],[],[]]
aut_to_classify=[]
for key,value in dict_frequency_2.items():
if value==(0,0,0,0):
aut_to_classify.append(key)
else:
aut_classified[np.argmax(value)].append(key)
min_cat=min(len(aut_classified[0]),len(aut_classified[1]),len(aut_classified[2]),len(aut_classified[3]))
aut_algo_temp=sorted(aut_classified[0], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[0])-set(aut_algo_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[0]=aut_algo_temp
aut_formal_temp=sorted(aut_classified[1], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[1])-set(aut_formal_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[1]=aut_formal_temp
aut_net_temp=sorted(aut_classified[2], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[2])-set(aut_net_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[2]=aut_net_temp
aut_crypto_temp=sorted(aut_classified[3], key=lambda aut:dict_centrality[aut],reverse=True)[0:min_cat]
for aut in list(set(aut_classified[3])-set(aut_crypto_temp)):
dict_frequency_2[aut]=(0,0,0,0)
aut_to_classify.append(aut)
aut_classified[3]=aut_crypto_temp
for aut in aut_classified:
print(len(aut))
415 415 415 415
map_auth_consolidate = {}
for i in range(1,max_v+1):
if (i in aut_classified[0] or i in aut_classified[1] or i in aut_classified[2] or i in aut_classified[3]):
if (i in aut_classified[0]):
map_auth_consolidate[i]=5000
elif (i in aut_classified[1]):
map_auth_consolidate[i]=6000
elif (i in aut_classified[2]):
map_auth_consolidate[i]=7000
else:
map_auth_consolidate[i]=8000
else:
map_auth_consolidate[i]=i
file=open(r"C:\Users\Daniele\Desktop\Graph mining\icalp.tg")
tg=pp.TemporalNetwork()
for line in file:
l=line.split()
u=int(l[0])
v=int(l[1])
time=int(l[2])
if (u in largest_cc and v in largest_cc):
if ( map_auth_consolidate[u] == map_auth_consolidate[v] ):
continue
tg.add_edge(map_auth_consolidate[u],map_auth_consolidate[v],time)
tg.add_edge(map_auth_consolidate[v],map_auth_consolidate[u],time)
print(tg)
Nodes: 1802 Time-stamped links: 10830 Links/Nodes: 6.009988901220866 Observation period: [1972, 2021] Observation length: 49 Time stamps: 48 Avg. inter-event dt: 1.0425531914893618 Min/Max inter-event dt: 1/2
# # #started 02:00
# for vertex in aut_to_classify:
# dict_frequency_3[vertex]=get_frequencies(tg,l=7,n=3000,start_node=vertex,end_nodes=[5000,6000,7000,8000])
# #end
# f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies3.txt", "w")
# f.write( str(dict_frequency_3) )
# f.close()
f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpAuthorDictFrequencies3.txt", "r")
contents = f.read()
dict_frequency_3 = ast.literal_eval(contents)
f.close()
dict_classification={}
for vertex in dict_frequency.keys():
if (dict_frequency_3[vertex]==(0,0,0,0)) or (sorted(dict_frequency_3[vertex],reverse=True)[0]- sorted(dict_frequency_3[vertex],reverse=True)[1] <0.2):
dict_classification[vertex]=4
continue
dict_classification[vertex]=np.argmax(dict_frequency_3[vertex])
nc=0
und=0
na=0
nfm=0
ncr=0
nn=0
for v in [ v for v in largest_cc if v not in authors_chosen ]:
if(dict_classification[v]==4):
nc=nc+1
if dict_frequency_3[v]!=(0,0,0,0):
und+=1
continue
if(dict_classification[v]==0):
na=na+1
if(dict_classification[v]==1):
nfm=nfm+1
if(dict_classification[v]==2):
nn=nn+1
if(dict_classification[v]==3):
ncr=ncr+1
print("Non classified authors = "+str(nc)+"\nUndetermined authors = "+str(und)+"\nAlgorithms authors = "+str(na)+"\nFormal methods authors = "+str(nfm)+"\nNetworks authors = "+str(nn)+"\nCryptography authors = "+str(ncr))
Non classified authors = 736 Undetermined authors = 202 Algorithms authors = 1141 Formal methods authors = 698 Networks authors = 457 Cryptography authors = 406
matrix=np.load(r"C:\Users\Daniele\Desktop\Graph mining\graphs\icalpw\icalpw2021_mvsMatrix.npy")
np.shape(matrix)[1]
3458
arr_names=[]
for i in range(np.shape(matrix)[1]):
arr_names.append(dict_names[id_cc[i]])
colors=["red","blue","green","yellow","black"]
arr_color=[]
for i in range(np.shape(matrix)[1]):
arr_color.append(colors[dict_classification[id_cc[i]]])
fig=go.Figure(data=[go.Scatter3d(x=matrix[0],y=matrix[1],z=matrix[2],text=arr_names,mode='markers',marker=dict(
color=arr_color,
size=3,
sizemode='diameter'
)
)])
fig.show()
# TAKE 5 MIN TO BE COMPUTED
# matrix=nx.algorithms.shortest_paths.dense.floyd_warshall_numpy(G)
# from sklearn.manifold import MDS
# embedding = MDS(n_components=3)
# G_transform = embedding.fit_transform(matrix)
# f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpMatrixMVSPython.txt", "w")
# for line in G_transform:
# np.savetxt(f,line,fmt='%.2e')
# f.close()
f = open(r"C:\Users\Daniele\Desktop\Graph mining\icalpMatrixMVSPython.txt", "r")
G_transform= np.loadtxt(f)
f.close()
G_transform= np.reshape(G_transform,(-1,3))
fig=go.Figure(data=[go.Scatter3d(x=G_transform[:,0],y=G_transform[:,1],z=G_transform[:,2],text=arr_names,mode='markers',marker=dict(
color=arr_color,
size=3,
sizemode='diameter'
)
)])
fig.show()